Tim Lucas

Tim Lucas
University of Leicester | LE · Department of Health Sciences

PhD

About

56
Publications
26,037
Reads
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6,509
Citations
Citations since 2016
52 Research Items
6493 Citations
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Introduction
I'm a lecturer at the Department of Health Sciences, University of Leicester. I develop statistical methods for studying disease at multiple time scales such as methods for accounting for human movement. This work follows on from 1 year as an ERC fellow at Imperial college. Before that I was a postdoc at the University of Oxford developing statistical methods for studying neglected tropical diseases and malaria. www.twitter.com/statsforbios http://timcdlucas.github.io
Additional affiliations
September 2013 - September 2016
University College London
Position
  • PhD Student
Description
  • I studied network models of disease spread in bat populations. In particular I examined pathogen competition and factors that promote high pathogen richness in bats.
April 2013 - present
University College London
Position
  • Online Tutor
Description
  • I am an online tutor for Sysmic, teaching mathematics and programming skills to life science researchers.
Education
September 2013 - December 2016
University College London
Field of study
  • Infectious Disease Modelling in Bat Populations
September 2011 - August 2012
University College London
Field of study
  • Modelling Biological Complexity
September 2006 - August 2010
The University of Sheffield
Field of study
  • Zoology

Publications

Publications (56)
Article
Full-text available
Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first globa...
Article
Full-text available
Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vec...
Preprint
Full-text available
Background Children discharged from hospital after recovery from severe malarial anaemia (SMA) are at high risk of readmission and death in subsequent months. Clinical trial results show that three months of post-discharge malaria chemoprevention (PMC) with dihydroartemisinin-piperaquine reduces this risk. Methods We developed a compartmental math...
Article
Full-text available
Emerging evidence suggests that contact tracing has had limited success in the UK in reducing the R number across the COVID-19 pandemic. We investigate potential pitfalls and areas for improvement by extending an existing branching process contact tracing model, adding diagnostic testing and refining parameter estimates. Our results demonstrate tha...
Article
Full-text available
Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modeling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between c...
Article
As the COVID‐19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID‐19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in Ch...
Article
Contact tracing is an important tool for allowing countries to ease lockdown policies introduced to combat SARS-CoV-2. For contact tracing to be effective, those with symptoms must self-report themselves while their contacts must self-isolate when asked. However, policies such as legal enforcement of self-isolation can create trade-offs by dissuadi...
Article
Full-text available
The dynamics of immunity are crucial to understanding the long-term patterns of the SARS-CoV-2 pandemic. Several cases of reinfection with SARS-CoV-2 have been documented 48–142 days after the initial infection and immunity to seasonal circulating coronaviruses is estimated to be shorter than 1 year. Using an age-structured, deterministic model, we...
Article
Full-text available
Several thousand people die every year worldwide because of terrorist attacks perpetrated by non-state actors. In this context, reliable and accurate short-term predictions of non-state terrorism at the local level are key for policy makers to target preventative measures. Using only publicly available data, we show that predictive models that incl...
Article
Full-text available
As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low‐prevalence areas are increasingly needed. For low‐burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons. However...
Article
Full-text available
A stochastic individual based model, SCHISTOX, has been developed for the study of schistosome transmission dynamics and the impact of control by mass drug administration. More novel aspects that can be investigated include individual level adherence and access to treatment, multiple communities, human sex population dynamics, and implementation of...
Article
Full-text available
Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically...
Article
Full-text available
Background: Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial dr...
Article
Full-text available
Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitt...
Preprint
Full-text available
Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID- 19 and Ebola and endemic diseases such as malaria and tuberculosis. Yet a single coding bug may bias results, leading to incorrect conclusions and wrong actions that could cause avoidable...
Article
Full-text available
Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is becoming more widely available. Disaggregation regression is an important model framework for estimating high resolution risk maps from aggregated data. H...
Preprint
Full-text available
As the COVID-19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID-19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in Ch...
Preprint
Full-text available
Background: Following a consistent decline in COVID-19-related deaths in the UK throughout May 2020, it is recognised that contact tracing will be vital to relaxing physical distancing measures. The increasingly evident role of asymptomatic and pre-symptomatic transmission means testing is central to control, but test sensitivity estimates are as l...
Article
Machine learning has become popular in ecology but its use has remained restricted to predicting, rather than understanding, the natural world. Many researchers consider machine learning algorithms to be a black box. These models can however, with careful examination, be used to inform our understanding of the world. They are translucent boxes. Fur...
Preprint
Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modelling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between...
Preprint
Full-text available
Statistical analyses proceed by an iterative process of model fitting and checking. The R-INLA package facilitates this iteration by fitting many Bayesian models much faster than alternative MCMC approaches. As the interpretation of results and model objects from Bayesian analyses can be complex, the R package INLAutils provides users with easily a...
Article
Full-text available
Background: Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed t...
Preprint
Full-text available
Disaggregation modelling, or downscaling, has become an important discipline in epidemiology. Surveillance data, aggregated over large regions, is becoming more common, leading to an increasing demand for modelling frameworks that can deal with this data to understand spatial patterns. Disaggregation regression models use response data aggregated o...
Article
Full-text available
Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their prop...
Article
Full-text available
Background: Since 2000, the scale-up of malaria control interventions has substantially reduced morbidity and mortality caused by the disease globally, fuelling bold aims for disease elimination. In tandem with increased availability of geospatially resolved data, malaria control programmes increasingly use high-resolution maps to characterise spa...
Article
Full-text available
Background: Plasmodium vivax exacts a significant toll on health worldwide, yet few efforts to date have quantified the extent and temporal trends of its global distribution. Given the challenges associated with the proper diagnosis and treatment of P vivax, national malaria programmes-particularly those pursuing malaria elimination strategies-req...
Preprint
Full-text available
Maps of infection risk are a vital tool for the elimination of malaria. Routine surveillance data of malaria case counts, often aggregated over administrative regions, is becoming more widely available and can better measure low malaria risk than prevalence surveys. However, aggregation of case counts over large, heterogeneous areas means that thes...
Article
Summary Background: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improv...
Article
Full-text available
Background: The Malaria Atlas Project (MAP) has worked to assemble and maintain a global open-access database of spatial malariometric data for over a decade. This data spans various formats and topics, including: geo-located surveys of malaria parasite rate; global administrative boundary shapefiles; and global and regional rasters representing t...
Preprint
Full-text available
Individuals infected with the Plasmodium falciparum malaria parasite can carry multiple strains with varying levels of relatedness. Yet, how parameters of local epidemiology and the biology of transmission affect the rate and relatedness of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from geno...
Preprint
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new inputs much more difficult. We c...
Article
Full-text available
The economic and man-made resources that sustain human wellbeing are not distributed evenly across the world, but are instead heavily concentrated in cities. Poor access to opportunities and services offered by urban centres (a function of distance, transport infrastructure, and the spatial distribution of cities) is a major barrier to improved liv...
Data
Predictive accuracy of boosted regression trees (BRT) species distribution modelling method when varying the complexity of the underlying regression trees during inference. Points represent mean AUC score over a set of 5000 simulated species where a prediction of the true range is attempted using a set of simulated sampling points, with whiskers sh...
Data
Options tested for four different species distribution model methods (for details see text). Letters in ‘Setting’ columns indicate abbreviations used and numbers previous analyses from the literature. (DOCX)
Data
Predictive accuracy of the MAXENT species distribution modelling method when varying the complexity of the inference models using the beta, or “regularisation”, coefficient. Points represent mean AUC score over a set of 5000 simulated species where a prediction of the true range is attempted using a set of simulated sampling points, with whiskers s...
Data
Predictive accuracy of boosted regression trees (BRT) species distribution modelling method when varying the bag fraction used to hold-back parts of data for internal validation. Points represent mean AUC score over a set of 5000 simulated species where a prediction of the true range is attempted using a set of simulated sampling points, with whisk...
Data
Predictive accuracy of three species distribution modelling methods to infer 5000 simulated species’ ranges that were generated using either (a) just additive or (b) additive and interaction terms in formula used to determine the relationship between species’ presence and a set of simulated covariates. A repeated set of comparisons (c-d) is made fo...
Data
Predictive accuracy of the MAXENT species distribution modelling method when varying what class of terms are included in the inference model. Letter labels on x-axis represent model terms (Hinge—H, Product—P, Quadratic—Q, Threshold—T, Linear—L, Auto Feature—AF). Points represent mean AUC score over a set of 5000 simulated species where a prediction...
Data
Predictive accuracy of boosted regression trees (BRT) species distribution modelling method when varying the learning rate of tree inference algorithm. Points represent mean AUC score over a set of 5000 simulated species where a prediction of the true range is attempted using a set of simulated sampling points, with whiskers showing the 95% confide...
Data
Predictive accuracy of boosted regression trees (BRT) species distribution modelling method when varying the number of regression trees retained in the final modelling set. Points represent mean AUC score over a set of 5000 simulated species where a prediction of the true range is attempted using a set of simulated sampling points, with whiskers sh...
Article
Full-text available
1. Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often non-randomly distributed and geographically restricted. Although available SDM methods address some of these problems, the errors could be more directly and accurately modelled using...
Article
Full-text available
Background: As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years l...
Article
The rapid growth of species distribution modelling (SDM) as an ecological discipline has resulted in a large and diverse set of methods and software for constructing and evaluating SDMs. The disjointed nature of the current SDM research environment hinders evaluation of new methods, synthesis of current knowledge and the dissemination of new method...
Preprint
Zoonotic diseases are an increasingly important source of human infectious diseases, and host pathogen richness of reservoir host species is a critical driver of spill-over risk. Population-level traits of hosts such as population size, host density and geographic range size have all been shown to be important determinants of host pathogen richness...
Preprint
Full-text available
1. Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often non-randomly distributed and geographically restricted. Although available SDM methods address some of these problems, the errors could be more directly and accurately modelled using...
Thesis
Full-text available
Pathogens acquired from animals make up the majority of emerging human diseases, are often highly virulent and can have large effects on public health and economic development. Identifying species with high pathogen species richness enables efficient sampling and monitoring of potentially dangerous pathogens. I examine the role of host population s...
Article
Full-text available
Acoustic detectors are commonly being used to monitor wildlife. Current estimators of abundance or density require recognition of individuals or the distance of the animal from the sensor, which is often difficult. The random encounter model (REM) has been successfully applied to count data without these requirements. However, count data from acous...
Chapter
Full-text available
As bats are important biodiversity indicators, monitoring their populations is becoming increasingly important to understand the impacts of global change. Bats leak information about themselves into the environment in the form of ultrasonic calls. Using these calls to globally survey bat populations may offer a more efficient alternative or additio...
Thesis
Full-text available
Using a machine learning framework to construct an automatic, acoustic classifier for bats is a research and conservation priority for this large and often vulnerable group of animals. Here I use a large dataset of 1,340 calls from 34 of the 40 European species to construct a Gaussian process classifier. I obtain accuracies of 54.6% at the species...

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